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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2004: Advances in Knowledge Discovery and Data Mining pp 564–568Cite as

Predicting Web Requests Efficiently Using a Probability Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3056))

Abstract

As the world-wide-web grows rapidly and a user’s browsing experiences are needed to be personalized, the problem of predicting a user’s behavior on a web-site has become important. We present a probability model to utilize path profiles of users from web logs to predict the user’s future requests. Each of the user’s next probable requests is given a conditional probability value, which is calculated according to the function presented by us. Our model can give several predictions ranked by the values of their probability instead of giving one, thus increasing recommending ability. The experiments show that our algorithm and model has a good performance. The result can potentially be applied to a wide range of applications on the web.

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References

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Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, P.R.China

    Shanchan Wu & Wenyuan Wang

Authors
  1. Shanchan Wu
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  2. Wenyuan Wang
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Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Deakin University, VIC 3125, Australia

    Honghua Dai

  2. University of Illinois at Urbana-Champaign, 61801, Urbana, IL, USA

    Ramakrishnan Srikant

  3. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

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© 2004 Springer-Verlag Berlin Heidelberg

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Wu, S., Wang, W. (2004). Predicting Web Requests Efficiently Using a Probability Model. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_67

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  • DOI: https://doi.org/10.1007/978-3-540-24775-3_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22064-0

  • Online ISBN: 978-3-540-24775-3

  • eBook Packages: Springer Book Archive

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